In processing a digital image, it is common to sharpen the image and enhance fine detail with sharpening algorithms. Typically, this sharpening is performed by a convolution process (for example, see A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall: 1989, pp. 249–251). The process of unsharp masking is an example of a convolution-based sharpening process. For example, sharpening an image with unsharp masking can be described with the equation:s(x,y)=i(x,y)**b(x,y)+βf(i(x,y)−i(x,y)**b(x,y))  (1)where:                s(x,y)=output image with enhanced sharpness        i(x,y)=original input image        b(x,y)=lowpass filter        β=unsharp mask scale factor        f( )=fringe function        ** denotes two dimensional convolution        (x,y) denotes the xth row and the yth column of an image        
Typically, an unsharp image is generated by convolution of the image with a lowpass filter (i.e., the unsharp image is given by i(x,y)**b(x,y)). Next, the highpass, or fringe, data is generated by subtracting the unsharp image from the original image (i.e., the highpass data is found with i(x,y)−i(x,y)**b(x,y)). This highpass data is then modified by either a scale factor β or a fringe function f( ) or both. Finally, the modified highpass data is summed with either the original image or the unsharp image to produce a sharpened image.
A similar sharpening effect can be achieved by modification of the image in the frequency domain (for example, the FFT domain) as is well known in the art of digital signal processing. Both the space domain (e.g., convolution methods) and the frequency domain methods of enhancing image sharpness are shift invariant methods. In other words, the sharpening process is invariant to the location within the image.
While these methods do indeed produce sharpened images, the quality of the resulting image often varies depending on the image content. For example, using the unsharp mask algorithm may produce a pleasing result for an image of a building. However, using the same algorithm may result in the undesirable appearance of oversharpening for an image of a human face (e.g., blemishes may be enhanced). The scale factor parameter may be modified individually for each scene by a human operator, but this is an expensive process.
In U.S. Pat. No. 5,682,443, Gouch and MacDonald describe a method of modifying, on a pixel by pixel basis, the parameters associated with the unsharp mask. In essence, the constant scale factor β in equation (1) is replaced with a scale factor which varies based on location β(x,y). These parameters are varied based on the color of the pixels in a local neighborhood. The method allows for the de-emphasis of the detail for pixels which are approximately flesh colored. This method is not shift invariant, however, since the fringe data is modified with a weighting function determined in accordance with the values of the sharp or unsharp data for each of the color components of each pixel. Consequently, this method is computationally intensive because the filter parameters are varied for each pixel. Additionally, this method can produce switching artifacts when one region of an image is sharpened far more or less than a nearby region.
Therefore, there exists a need for quickly sharpening, or otherwise improving, an image whereby the overall improvement of the image can be adjusted based on the material content of the image, and without the production of switching artifacts.